CSE343, Machine Learning Course Project, IIIT Delhi, Monsoon 2021

This repository demonstrates how data science can help to identify the employee attrition which is part of Human Resource Management

This project involves Employee Attrition Prediction using various data visualisation techniques & machine learning models. The repository consists of the .ipynb file and files used for deploying the ML model on 'Heroku' using the Flask framework.

This project is a machine learning classification problem. The objective of this project was to predict the rate of employee attrition in the current scenario based on different features. It was the classification problem. I tried three algorithms (Logistics, Decision Tree & Random Forest). But I got high accuracy score about 0.97 using random Forest.

Bill Gates was once quoted as saying, "You take away our top 20 employees and we [Microsoft] become a mediocre company". This statement by Bill Gates took our attention to one of the major problems of employee attrition at workplaces. Employee attrition (turnover) causes a significant cost to any organization which may later on effect its overall efficiency. As per CompData Surveys, over the past five years, total turnover has increased from 15.1 percent to 18.5 percent. For any organization, finding a well trained and experienced employee is a complex task, but it’s even more complex to replace such employees. This not only increases the significant Human Resource (HR) cost, but also impacts the market value of an organization. Despite these facts and ground reality, there is little attention to the literature, which has been seeded to many misconceptions between HR and Employees. Therefore, the aim of this paper is to provide a framework for predicting the employee churn by analyzing the employee’s precise behaviors and attributes using classification techniques.

This app allows users to explore key factors for employee attrition. Survey data can be filtered by gender, age, and department.

Uncover the factors that lead to employee attrition using IBM Employee Data

In this project, the team strives to use machine learning principles to predict employee attrition, provide managerial insights to prevent attrition, and finally rule out and present the factors that lead to attrition.

Clustering employee performances to predict resignation likelihood and develop strategies for employee retention

In this project I did Complete EDA, and Build a ML model that can accurately predict whether an Employee will be leave a company or not based on different factors.

A Power BI Dashboard analyzing employee attrition to explore key factors behind employee turnover.

An HR Analytics Dashboard built using Tableau to analyze employee attrition trends and provide actionable workforce insights.

In the repository project regarding employee resignation prediction using ensemble learning and tree-based machine learning models with FastAPI.

Uncover the factors that lead to employee attrition at IBM

Power BI dashboard analyzing employee attrition and retention drivers

End-to-End Data Analyst project on employee attrition using Python, MySQL, and Logistic Regression. Covers full EDA, SQL analytics, and predictive modeling for HR decision-making, risk segmentation, business KPIs, and actionable retention strategy.

HR Analytics Dashboard | Attrition Prediction | Power BI Style | HR Assistant

Interactive Power BI dashboard analyzing employee attrition trends and workforce insights for data-driven HR decisions.

machine learning based hr attrition prediction system built using python, scikit-learn and streamlit. the project analyzes employee data, predicts attrition risk, and provides interactive analytics dashboard with model insights and feature importance visualization.

Enterprise-grade HR Analytics Dashboard with Machine Learning - Power BI + Python integration for employee attrition prediction and workforce insights

Exploratory data analysis and machine learning classification models to predict employee attrition.

This project focuses on predicting the attrition rate of employees by using different ML algorithms. The dataset is a fictional data taken from Kaggle

Understanding and predicting employee's attrition

Analyzing 1,470 IBM employees to find why they leave — Python, SQL, Interactive Dashboard

A Logistic Regression system that predicts employees at risk of leaving before they actually do.

This project addresses two interrelated machine learning tasks in the field of HR analytics for the company "Care with Work"

Predicting why employees are leaving organization & building a model to predict in future, who will leave the company.

📊 Predict employee attrition using ML 🤖 | Analyze HR data to find who might leave & help take action early ✅

End-to-end HR Analytics and Employee Attrition Prediction app built with Python and Streamlit. Includes EDA, ML models, and interactive dashboards.

This project analyzes employee attrition at Salifort Motors using machine learning and data analytics to identify key turnover drivers. The analysis spans data cleaning, exploratory data analysis (EDA), predictive modeling (logistic regression, decision trees, random forest, and XGBoost), and actionable HR recommendations.